Overview

Dataset statistics

Number of variables23
Number of observations32833
Missing cells15
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 MiB
Average record size in memory184.0 B

Variable types

Text7
Numeric12
DateTime1
Categorical3

Alerts

track_popularity has 2703 (8.2%) zerosZeros
key has 3454 (10.5%) zerosZeros
instrumentalness has 12089 (36.8%) zerosZeros

Reproduction

Analysis started2024-01-26 18:19:54.682110
Analysis finished2024-01-26 18:20:08.871435
Duration14.19 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct28356
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:09.006483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters722326
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25190 ?
Unique (%)76.7%

Sample

1st row6f807x0ima9a1j3VPbc7VN
2nd row0r7CVbZTWZgbTCYdfa2P31
3rd row1z1Hg7Vb0AhHDiEmnDE79l
4th row75FpbthrwQmzHlBJLuGdC7
5th row1e8PAfcKUYoKkxPhrHqw4x
ValueCountFrequency (%)
7bklcz1jbubvqri2fvltvw 10
 
< 0.1%
3eekarcy7kvn4yt5zfzltw 9
 
< 0.1%
14sos5l36385fj3ol8hew4 9
 
< 0.1%
0sf12qnh5qcw8qpgymfoqd 8
 
< 0.1%
0nbxyq5txypco7pr3n8s4i 8
 
< 0.1%
7lzouawgfcy4tkxdooneym 8
 
< 0.1%
7h0d2h0fumzbs7zefigjpn 8
 
< 0.1%
56amygjzxbo6p8v0wee9de 8
 
< 0.1%
6wri0lac5m1rw2mnx2zveg 8
 
< 0.1%
6gg1gjgki2ak4e0qzsr7sd 8
 
< 0.1%
Other values (28346) 32749
99.7%
2024-01-26T15:20:09.284645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15445
 
2.1%
6 15375
 
2.1%
3 15304
 
2.1%
4 15295
 
2.1%
2 15212
 
2.1%
1 15190
 
2.1%
5 15170
 
2.1%
7 14649
 
2.0%
C 11345
 
1.6%
R 11338
 
1.6%
Other values (52) 578003
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 289659
40.1%
Uppercase Letter 288696
40.0%
Decimal Number 143971
19.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 11345
 
3.9%
R 11338
 
3.9%
F 11286
 
3.9%
K 11261
 
3.9%
O 11245
 
3.9%
Q 11244
 
3.9%
J 11221
 
3.9%
E 11201
 
3.9%
D 11151
 
3.9%
X 11140
 
3.9%
Other values (16) 176264
61.1%
Lowercase Letter
ValueCountFrequency (%)
e 11326
 
3.9%
q 11300
 
3.9%
y 11293
 
3.9%
m 11275
 
3.9%
s 11235
 
3.9%
a 11227
 
3.9%
t 11221
 
3.9%
p 11212
 
3.9%
z 11205
 
3.9%
w 11189
 
3.9%
Other values (16) 177176
61.2%
Decimal Number
ValueCountFrequency (%)
0 15445
10.7%
6 15375
10.7%
3 15304
10.6%
4 15295
10.6%
2 15212
10.6%
1 15190
10.6%
5 15170
10.5%
7 14649
10.2%
9 11194
7.8%
8 11137
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 578355
80.1%
Common 143971
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 11345
 
2.0%
R 11338
 
2.0%
e 11326
 
2.0%
q 11300
 
2.0%
y 11293
 
2.0%
F 11286
 
2.0%
m 11275
 
1.9%
K 11261
 
1.9%
O 11245
 
1.9%
Q 11244
 
1.9%
Other values (42) 465442
80.5%
Common
ValueCountFrequency (%)
0 15445
10.7%
6 15375
10.7%
3 15304
10.6%
4 15295
10.6%
2 15212
10.6%
1 15190
10.6%
5 15170
10.5%
7 14649
10.2%
9 11194
7.8%
8 11137
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 722326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15445
 
2.1%
6 15375
 
2.1%
3 15304
 
2.1%
4 15295
 
2.1%
2 15212
 
2.1%
1 15190
 
2.1%
5 15170
 
2.1%
7 14649
 
2.0%
C 11345
 
1.6%
R 11338
 
1.6%
Other values (52) 578003
80.0%
Distinct23449
Distinct (%)71.4%
Missing5
Missing (%)< 0.1%
Memory size256.6 KiB
2024-01-26T15:20:09.532744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length144
Median length94
Mean length17.379859
Min length1

Characters and Unicode

Total characters570546
Distinct characters173
Distinct categories19 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18468 ?
Unique (%)56.3%

Sample

1st rowI Don't Care (with Justin Bieber) - Loud Luxury Remix
2nd rowMemories - Dillon Francis Remix
3rd rowAll the Time - Don Diablo Remix
4th rowCall You Mine - Keanu Silva Remix
5th rowSomeone You Loved - Future Humans Remix
ValueCountFrequency (%)
6154
 
5.6%
feat 2804
 
2.6%
the 2681
 
2.5%
remix 2108
 
1.9%
you 1880
 
1.7%
me 1629
 
1.5%
i 1163
 
1.1%
love 1135
 
1.0%
a 850
 
0.8%
to 845
 
0.8%
Other values (15635) 88156
80.6%
2024-01-26T15:20:09.921964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
76543
 
13.4%
e 51413
 
9.0%
a 34941
 
6.1%
o 33285
 
5.8%
i 30273
 
5.3%
t 25793
 
4.5%
n 25222
 
4.4%
r 22418
 
3.9%
l 17202
 
3.0%
s 16539
 
2.9%
Other values (163) 236917
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 358237
62.8%
Uppercase Letter 103412
 
18.1%
Space Separator 76570
 
13.4%
Other Punctuation 9602
 
1.7%
Dash Punctuation 5280
 
0.9%
Decimal Number 5045
 
0.9%
Open Punctuation 4480
 
0.8%
Close Punctuation 4479
 
0.8%
Control 1285
 
0.2%
Other Number 579
 
0.1%
Other values (9) 1577
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 51413
14.4%
a 34941
 
9.8%
o 33285
 
9.3%
i 30273
 
8.5%
t 25793
 
7.2%
n 25222
 
7.0%
r 22418
 
6.3%
l 17202
 
4.8%
s 16539
 
4.6%
u 12494
 
3.5%
Other values (29) 88657
24.7%
Uppercase Letter
ValueCountFrequency (%)
S 7686
 
7.4%
M 7650
 
7.4%
T 7456
 
7.2%
R 7191
 
7.0%
L 6093
 
5.9%
A 5504
 
5.3%
B 5455
 
5.3%
D 5081
 
4.9%
I 4828
 
4.7%
C 4725
 
4.6%
Other values (23) 41743
40.4%
Control
ValueCountFrequency (%)
€ 180
14.0%
ƒ 157
 
12.2%
‚ 133
 
10.4%
 129
 
10.0%
™ 96
 
7.5%
œ 48
 
3.7%
‹ 35
 
2.7%
‰ 33
 
2.6%
Ÿ 33
 
2.6%
 32
 
2.5%
Other values (22) 409
31.8%
Other Punctuation
ValueCountFrequency (%)
. 3915
40.8%
' 2575
26.8%
& 1105
 
11.5%
, 731
 
7.6%
" 239
 
2.5%
¡ 214
 
2.2%
/ 172
 
1.8%
! 171
 
1.8%
? 168
 
1.7%
* 73
 
0.8%
Other values (9) 239
 
2.5%
Decimal Number
ValueCountFrequency (%)
0 1330
26.4%
2 1159
23.0%
1 1017
20.2%
9 368
 
7.3%
3 275
 
5.5%
4 218
 
4.3%
5 197
 
3.9%
7 177
 
3.5%
8 156
 
3.1%
6 148
 
2.9%
Math Symbol
ValueCountFrequency (%)
± 118
61.5%
+ 33
 
17.2%
× 18
 
9.4%
¬ 15
 
7.8%
~ 5
 
2.6%
| 2
 
1.0%
= 1
 
0.5%
Other Number
ValueCountFrequency (%)
³ 193
33.3%
¾ 124
21.4%
¼ 105
18.1%
½ 89
15.4%
¹ 36
 
6.2%
² 32
 
5.5%
Currency Symbol
ValueCountFrequency (%)
$ 89
42.4%
£ 49
23.3%
¤ 38
18.1%
¢ 18
 
8.6%
¥ 16
 
7.6%
Modifier Symbol
ValueCountFrequency (%)
¸ 88
45.4%
´ 71
36.6%
¨ 20
 
10.3%
¯ 12
 
6.2%
` 3
 
1.5%
Other Symbol
ValueCountFrequency (%)
© 261
53.5%
° 190
38.9%
® 22
 
4.5%
¦ 15
 
3.1%
Space Separator
ValueCountFrequency (%)
76543
> 99.9%
  27
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 4330
96.7%
[ 150
 
3.3%
Close Punctuation
ValueCountFrequency (%)
) 4329
96.7%
] 150
 
3.3%
Other Letter
ValueCountFrequency (%)
º 134
76.6%
ª 41
 
23.4%
Dash Punctuation
ValueCountFrequency (%)
- 5280
100.0%
Format
ValueCountFrequency (%)
­ 186
100.0%
Final Punctuation
ValueCountFrequency (%)
» 65
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 65
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 461749
80.9%
Common 108797
 
19.1%

Most frequent character per script

Common
ValueCountFrequency (%)
76543
70.4%
- 5280
 
4.9%
( 4330
 
4.0%
) 4329
 
4.0%
. 3915
 
3.6%
' 2575
 
2.4%
0 1330
 
1.2%
2 1159
 
1.1%
& 1105
 
1.0%
1 1017
 
0.9%
Other values (90) 7214
 
6.6%
Latin
ValueCountFrequency (%)
e 51413
 
11.1%
a 34941
 
7.6%
o 33285
 
7.2%
i 30273
 
6.6%
t 25793
 
5.6%
n 25222
 
5.5%
r 22418
 
4.9%
l 17202
 
3.7%
s 16539
 
3.6%
u 12494
 
2.7%
Other values (63) 192169
41.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 563570
98.8%
None 6976
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
76543
 
13.6%
e 51413
 
9.1%
a 34941
 
6.2%
o 33285
 
5.9%
i 30273
 
5.4%
t 25793
 
4.6%
n 25222
 
4.5%
r 22418
 
4.0%
l 17202
 
3.1%
s 16539
 
2.9%
Other values (79) 229941
40.8%
None
ValueCountFrequency (%)
à 1300
18.6%
Ð 1039
 
14.9%
Ñ 347
 
5.0%
© 261
 
3.7%
ã 221
 
3.2%
¡ 214
 
3.1%
³ 193
 
2.8%
° 190
 
2.7%
­ 186
 
2.7%
€ 180
 
2.6%
Other values (74) 2845
40.8%
Distinct10692
Distinct (%)32.6%
Missing5
Missing (%)< 0.1%
Memory size256.6 KiB
2024-01-26T15:20:10.151254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length69
Median length43
Mean length10.107804
Min length2

Characters and Unicode

Total characters331819
Distinct characters163
Distinct categories19 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6141 ?
Unique (%)18.7%

Sample

1st rowEd Sheeran
2nd rowMaroon 5
3rd rowZara Larsson
4th rowThe Chainsmokers
5th rowLewis Capaldi
ValueCountFrequency (%)
the 1774
 
3.0%
778
 
1.3%
dj 236
 
0.4%
martin 229
 
0.4%
mike 221
 
0.4%
lil 218
 
0.4%
j 189
 
0.3%
david 178
 
0.3%
of 171
 
0.3%
garrix 162
 
0.3%
Other values (11596) 54183
92.9%
2024-01-26T15:20:10.523287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 28721
 
8.7%
a 26552
 
8.0%
25496
 
7.7%
i 19360
 
5.8%
n 18592
 
5.6%
o 18495
 
5.6%
r 16806
 
5.1%
l 13959
 
4.2%
s 12188
 
3.7%
t 11038
 
3.3%
Other values (153) 140612
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 229765
69.2%
Uppercase Letter 69508
 
20.9%
Space Separator 25510
 
7.7%
Other Punctuation 3061
 
0.9%
Decimal Number 1598
 
0.5%
Control 773
 
0.2%
Dash Punctuation 469
 
0.1%
Other Symbol 274
 
0.1%
Currency Symbol 259
 
0.1%
Other Number 176
 
0.1%
Other values (9) 426
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28721
12.5%
a 26552
11.6%
i 19360
 
8.4%
n 18592
 
8.1%
o 18495
 
8.0%
r 16806
 
7.3%
l 13959
 
6.1%
s 12188
 
5.3%
t 11038
 
4.8%
h 8043
 
3.5%
Other values (25) 56011
24.4%
Uppercase Letter
ValueCountFrequency (%)
S 5319
 
7.7%
M 5240
 
7.5%
T 4889
 
7.0%
C 4449
 
6.4%
B 4361
 
6.3%
A 4206
 
6.1%
D 4112
 
5.9%
L 3551
 
5.1%
R 3388
 
4.9%
J 3161
 
4.5%
Other values (22) 26832
38.6%
Control
ValueCountFrequency (%)
ƒ 224
29.0%
‚ 170
22.0%
– 67
 
8.7%
ˆ 49
 
6.3%
œ 39
 
5.0%
˜ 35
 
4.5%
€ 27
 
3.5%
‘ 23
 
3.0%
 21
 
2.7%
‹ 19
 
2.5%
Other values (21) 99
12.8%
Other Punctuation
ValueCountFrequency (%)
. 1315
43.0%
& 788
25.7%
' 307
 
10.0%
! 163
 
5.3%
¡ 146
 
4.8%
" 96
 
3.1%
66
 
2.2%
/ 61
 
2.0%
, 58
 
1.9%
: 25
 
0.8%
Other values (9) 36
 
1.2%
Decimal Number
ValueCountFrequency (%)
2 305
19.1%
1 248
15.5%
5 207
13.0%
3 189
11.8%
0 143
8.9%
8 127
7.9%
4 120
 
7.5%
6 97
 
6.1%
9 85
 
5.3%
7 77
 
4.8%
Other Number
ValueCountFrequency (%)
¼ 61
34.7%
³ 57
32.4%
½ 26
14.8%
¹ 16
 
9.1%
¾ 14
 
8.0%
² 2
 
1.1%
Currency Symbol
ValueCountFrequency (%)
$ 148
57.1%
¤ 72
27.8%
£ 21
 
8.1%
¥ 9
 
3.5%
¢ 9
 
3.5%
Other Symbol
ValueCountFrequency (%)
© 254
92.7%
° 12
 
4.4%
® 5
 
1.8%
¦ 3
 
1.1%
Math Symbol
ValueCountFrequency (%)
¬ 51
47.2%
± 32
29.6%
+ 21
19.4%
| 4
 
3.7%
Modifier Symbol
ValueCountFrequency (%)
¸ 26
66.7%
¯ 7
 
17.9%
´ 3
 
7.7%
¨ 3
 
7.7%
Space Separator
ValueCountFrequency (%)
25496
99.9%
  14
 
0.1%
Other Letter
ValueCountFrequency (%)
ª 61
79.2%
º 16
 
20.8%
Close Punctuation
ValueCountFrequency (%)
) 11
55.0%
] 9
45.0%
Open Punctuation
ValueCountFrequency (%)
( 11
55.0%
[ 9
45.0%
Dash Punctuation
ValueCountFrequency (%)
- 469
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 79
100.0%
Format
ValueCountFrequency (%)
­ 59
100.0%
Final Punctuation
ValueCountFrequency (%)
» 18
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 299327
90.2%
Common 32492
 
9.8%

Most frequent character per script

Common
ValueCountFrequency (%)
25496
78.5%
. 1315
 
4.0%
& 788
 
2.4%
- 469
 
1.4%
' 307
 
0.9%
2 305
 
0.9%
© 254
 
0.8%
1 248
 
0.8%
ƒ 224
 
0.7%
5 207
 
0.6%
Other values (85) 2879
 
8.9%
Latin
ValueCountFrequency (%)
e 28721
 
9.6%
a 26552
 
8.9%
i 19360
 
6.5%
n 18592
 
6.2%
o 18495
 
6.2%
r 16806
 
5.6%
l 13959
 
4.7%
s 12188
 
4.1%
t 11038
 
3.7%
h 8043
 
2.7%
Other values (58) 125573
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 328334
98.9%
None 3485
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 28721
 
8.7%
a 26552
 
8.1%
25496
 
7.8%
i 19360
 
5.9%
n 18592
 
5.7%
o 18495
 
5.6%
r 16806
 
5.1%
l 13959
 
4.3%
s 12188
 
3.7%
t 11038
 
3.4%
Other values (76) 137127
41.8%
None
ValueCountFrequency (%)
à 895
25.7%
ã 365
 
10.5%
© 254
 
7.3%
ƒ 224
 
6.4%
‚ 170
 
4.9%
¡ 146
 
4.2%
Ð 137
 
3.9%
« 79
 
2.3%
¤ 72
 
2.1%
– 67
 
1.9%
Other values (67) 1076
30.9%

track_popularity
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.477081
Minimum0
Maximum100
Zeros2703
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:10.655911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124
median45
Q362
95-th percentile79
Maximum100
Range100
Interquartile range (IQR)38

Descriptive statistics

Standard deviation24.984074
Coefficient of variation (CV)0.58817776
Kurtosis-0.93277039
Mean42.477081
Median Absolute Deviation (MAD)18
Skewness-0.23332007
Sum1394650
Variance624.20398
MonotonicityNot monotonic
2024-01-26T15:20:10.777183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2703
 
8.2%
1 575
 
1.8%
57 541
 
1.6%
60 514
 
1.6%
51 514
 
1.6%
54 514
 
1.6%
52 506
 
1.5%
45 505
 
1.5%
58 503
 
1.5%
50 498
 
1.5%
Other values (91) 25460
77.5%
ValueCountFrequency (%)
0 2703
8.2%
1 575
 
1.8%
2 387
 
1.2%
3 321
 
1.0%
4 240
 
0.7%
5 240
 
0.7%
6 192
 
0.6%
7 189
 
0.6%
8 201
 
0.6%
9 195
 
0.6%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 4
 
< 0.1%
98 36
0.1%
97 22
 
0.1%
96 7
 
< 0.1%
95 15
 
< 0.1%
94 37
0.1%
93 44
0.1%
92 27
0.1%
91 58
0.2%
Distinct22545
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:10.970465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters722326
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17545 ?
Unique (%)53.4%

Sample

1st row2oCs0DGTsRO98Gh5ZSl2Cx
2nd row63rPSO264uRjW1X5E6cWv6
3rd row1HoSmj2eLcsrR0vE9gThr4
4th row1nqYsOef1yKKuGOVchbsk6
5th row7m7vv9wlQ4i0LFuJiE2zsQ
ValueCountFrequency (%)
5l1xcowsxwzfusjzvymp48 42
 
0.1%
5fstcqs5npilf42vhpnv23 29
 
0.1%
7cjjb2mikwawa1v6kewfbf 28
 
0.1%
4vfg1doutedmbjblzt7hck 26
 
0.1%
2htbq0rhwukkvxaltmczp2 21
 
0.1%
4czt5uefbrpbilw34hqyxi 21
 
0.1%
6ylffzx32icw4l1a7ywnln 20
 
0.1%
246e5ovv4qxhprgosj7vdb 20
 
0.1%
5xcotqg63v60ns82pmqmbe 20
 
0.1%
0s0kgznfbgsissff54wsjh 18
 
0.1%
Other values (22535) 32588
99.3%
2024-01-26T15:20:11.361145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15957
 
2.2%
2 15554
 
2.2%
0 15513
 
2.1%
5 15509
 
2.1%
3 15467
 
2.1%
6 15368
 
2.1%
4 15075
 
2.1%
7 14325
 
2.0%
w 11443
 
1.6%
e 11355
 
1.6%
Other values (52) 576760
79.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 289018
40.0%
Uppercase Letter 288577
40.0%
Decimal Number 144731
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 11443
 
4.0%
e 11355
 
3.9%
x 11300
 
3.9%
p 11252
 
3.9%
v 11249
 
3.9%
k 11237
 
3.9%
m 11220
 
3.9%
f 11213
 
3.9%
d 11204
 
3.9%
q 11168
 
3.9%
Other values (16) 176377
61.0%
Uppercase Letter
ValueCountFrequency (%)
V 11331
 
3.9%
F 11298
 
3.9%
Z 11268
 
3.9%
D 11203
 
3.9%
B 11199
 
3.9%
G 11196
 
3.9%
H 11178
 
3.9%
N 11177
 
3.9%
M 11136
 
3.9%
L 11125
 
3.9%
Other values (16) 176466
61.2%
Decimal Number
ValueCountFrequency (%)
1 15957
11.0%
2 15554
10.7%
0 15513
10.7%
5 15509
10.7%
3 15467
10.7%
6 15368
10.6%
4 15075
10.4%
7 14325
9.9%
9 11193
7.7%
8 10770
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 577595
80.0%
Common 144731
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 11443
 
2.0%
e 11355
 
2.0%
V 11331
 
2.0%
x 11300
 
2.0%
F 11298
 
2.0%
Z 11268
 
2.0%
p 11252
 
1.9%
v 11249
 
1.9%
k 11237
 
1.9%
m 11220
 
1.9%
Other values (42) 464642
80.4%
Common
ValueCountFrequency (%)
1 15957
11.0%
2 15554
10.7%
0 15513
10.7%
5 15509
10.7%
3 15467
10.7%
6 15368
10.6%
4 15075
10.4%
7 14325
9.9%
9 11193
7.7%
8 10770
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 722326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15957
 
2.2%
2 15554
 
2.2%
0 15513
 
2.1%
5 15509
 
2.1%
3 15467
 
2.1%
6 15368
 
2.1%
4 15075
 
2.1%
7 14325
 
2.0%
w 11443
 
1.6%
e 11355
 
1.6%
Other values (52) 576760
79.8%
Distinct19743
Distinct (%)60.1%
Missing5
Missing (%)< 0.1%
Memory size256.6 KiB
2024-01-26T15:20:11.605940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length151
Median length102
Mean length17.5884
Min length1

Characters and Unicode

Total characters577392
Distinct characters177
Distinct categories19 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14210 ?
Unique (%)43.3%

Sample

1st rowI Don't Care (with Justin Bieber) [Loud Luxury Remix]
2nd rowMemories (Dillon Francis Remix)
3rd rowAll the Time (Don Diablo Remix)
4th rowCall You Mine - The Remixes
5th rowSomeone You Loved (Future Humans Remix)
ValueCountFrequency (%)
the 4566
 
4.5%
1928
 
1.9%
of 1576
 
1.5%
feat 1398
 
1.4%
remix 1119
 
1.1%
you 1064
 
1.0%
me 929
 
0.9%
deluxe 914
 
0.9%
a 837
 
0.8%
love 795
 
0.8%
Other values (14492) 87131
85.2%
2024-01-26T15:20:11.991440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69415
 
12.0%
e 54490
 
9.4%
o 32559
 
5.6%
a 31919
 
5.5%
i 30661
 
5.3%
t 26691
 
4.6%
n 25723
 
4.5%
r 24106
 
4.2%
s 21005
 
3.6%
l 19252
 
3.3%
Other values (167) 241571
41.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 367456
63.6%
Uppercase Letter 105721
 
18.3%
Space Separator 69433
 
12.0%
Other Punctuation 9600
 
1.7%
Decimal Number 7511
 
1.3%
Open Punctuation 6739
 
1.2%
Close Punctuation 6739
 
1.2%
Dash Punctuation 1223
 
0.2%
Control 1101
 
0.2%
Other Number 501
 
0.1%
Other values (9) 1368
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 54490
14.8%
o 32559
 
8.9%
a 31919
 
8.7%
i 30661
 
8.3%
t 26691
 
7.3%
n 25723
 
7.0%
r 24106
 
6.6%
s 21005
 
5.7%
l 19252
 
5.2%
h 12941
 
3.5%
Other values (28) 88109
24.0%
Uppercase Letter
ValueCountFrequency (%)
T 9423
 
8.9%
S 7753
 
7.3%
M 6535
 
6.2%
R 6384
 
6.0%
A 6172
 
5.8%
D 5802
 
5.5%
B 5561
 
5.3%
L 5333
 
5.0%
E 5211
 
4.9%
C 4982
 
4.7%
Other values (24) 42565
40.3%
Control
ValueCountFrequency (%)
€ 165
15.0%
‚ 123
 
11.2%
ƒ 107
 
9.7%
 99
 
9.0%
™ 77
 
7.0%
‰ 64
 
5.8%
œ 42
 
3.8%
‘ 34
 
3.1%
” 31
 
2.8%
‹ 30
 
2.7%
Other values (22) 329
29.9%
Other Punctuation
ValueCountFrequency (%)
. 3607
37.6%
' 1820
19.0%
& 1016
 
10.6%
, 949
 
9.9%
: 894
 
9.3%
/ 313
 
3.3%
! 244
 
2.5%
" 207
 
2.2%
? 187
 
1.9%
¡ 148
 
1.5%
Other values (9) 215
 
2.2%
Decimal Number
ValueCountFrequency (%)
1 1745
23.2%
0 1558
20.7%
2 1478
19.7%
9 679
 
9.0%
3 452
 
6.0%
4 404
 
5.4%
8 352
 
4.7%
7 318
 
4.2%
5 275
 
3.7%
6 250
 
3.3%
Math Symbol
ValueCountFrequency (%)
± 119
43.1%
+ 58
21.0%
× 55
19.9%
~ 20
 
7.2%
> 8
 
2.9%
| 5
 
1.8%
= 4
 
1.4%
¬ 4
 
1.4%
< 3
 
1.1%
Other Number
ValueCountFrequency (%)
³ 197
39.3%
¾ 97
19.4%
½ 75
 
15.0%
¼ 58
 
11.6%
¹ 47
 
9.4%
² 27
 
5.4%
Modifier Symbol
ValueCountFrequency (%)
¸ 76
50.7%
´ 52
34.7%
¨ 10
 
6.7%
¯ 6
 
4.0%
` 6
 
4.0%
Currency Symbol
ValueCountFrequency (%)
$ 74
43.5%
£ 53
31.2%
¤ 20
 
11.8%
¢ 15
 
8.8%
¥ 8
 
4.7%
Other Symbol
ValueCountFrequency (%)
© 192
52.3%
° 135
36.8%
¦ 25
 
6.8%
® 15
 
4.1%
Open Punctuation
ValueCountFrequency (%)
( 6231
92.5%
[ 507
 
7.5%
{ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 6231
92.5%
] 507
 
7.5%
} 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
69415
> 99.9%
  18
 
< 0.1%
Other Letter
ValueCountFrequency (%)
º 128
75.3%
ª 42
 
24.7%
Dash Punctuation
ValueCountFrequency (%)
- 1223
100.0%
Format
ValueCountFrequency (%)
­ 161
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 36
100.0%
Final Punctuation
ValueCountFrequency (%)
» 33
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 473264
82.0%
Common 104128
 
18.0%

Most frequent character per script

Common
ValueCountFrequency (%)
69415
66.7%
( 6231
 
6.0%
) 6231
 
6.0%
. 3607
 
3.5%
' 1820
 
1.7%
1 1745
 
1.7%
0 1558
 
1.5%
2 1478
 
1.4%
- 1223
 
1.2%
& 1016
 
1.0%
Other values (94) 9804
 
9.4%
Latin
ValueCountFrequency (%)
e 54490
 
11.5%
o 32559
 
6.9%
a 31919
 
6.7%
i 30661
 
6.5%
t 26691
 
5.6%
n 25723
 
5.4%
r 24106
 
5.1%
s 21005
 
4.4%
l 19252
 
4.1%
h 12941
 
2.7%
Other values (63) 193917
41.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 571544
99.0%
None 5848
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69415
 
12.1%
e 54490
 
9.5%
o 32559
 
5.7%
a 31919
 
5.6%
i 30661
 
5.4%
t 26691
 
4.7%
n 25723
 
4.5%
r 24106
 
4.2%
s 21005
 
3.7%
l 19252
 
3.4%
Other values (83) 235723
41.2%
None
ValueCountFrequency (%)
à 1147
19.6%
Ð 836
 
14.3%
Ñ 311
 
5.3%
³ 197
 
3.4%
© 192
 
3.3%
€ 165
 
2.8%
­ 161
 
2.8%
¡ 148
 
2.5%
ã 136
 
2.3%
° 135
 
2.3%
Other values (74) 2420
41.4%
Distinct4474
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
Minimum1957-01-01 00:00:00
Maximum2020-01-29 00:00:00
2024-01-26T15:20:12.123601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:12.252146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct449
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:12.434980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length128
Median length76
Mean length25.71885
Min length6

Characters and Unicode

Total characters844427
Distinct characters144
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPop Remix
2nd rowPop Remix
3rd rowPop Remix
4th rowPop Remix
5th rowPop Remix
ValueCountFrequency (%)
12734
 
9.0%
pop 4949
 
3.5%
rock 4706
 
3.3%
house 3404
 
2.4%
2020 2499
 
1.8%
hip 2448
 
1.7%
hits 2278
 
1.6%
rap 2168
 
1.5%
hop 2095
 
1.5%
edm 1997
 
1.4%
Other values (642) 102926
72.4%
2024-01-26T15:20:12.759884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
111011
 
13.1%
o 53679
 
6.4%
e 46675
 
5.5%
a 39825
 
4.7%
s 39279
 
4.7%
i 37137
 
4.4%
n 29115
 
3.4%
t 28876
 
3.4%
r 28395
 
3.4%
p 28050
 
3.3%
Other values (134) 402385
47.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 468427
55.5%
Uppercase Letter 158442
 
18.8%
Space Separator 111096
 
13.2%
Decimal Number 44636
 
5.3%
Control 21785
 
2.6%
Other Punctuation 18760
 
2.2%
Dash Punctuation 8080
 
1.0%
Math Symbol 4136
 
0.5%
Currency Symbol 3522
 
0.4%
Close Punctuation 1483
 
0.2%
Other values (7) 4060
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 53679
11.5%
e 46675
10.0%
a 39825
 
8.5%
s 39279
 
8.4%
i 37137
 
7.9%
n 29115
 
6.2%
t 28876
 
6.2%
r 28395
 
6.1%
p 28050
 
6.0%
c 21610
 
4.6%
Other values (21) 115786
24.7%
Uppercase Letter
ValueCountFrequency (%)
H 15553
 
9.8%
R 14672
 
9.3%
P 13754
 
8.7%
E 11744
 
7.4%
T 11464
 
7.2%
S 9478
 
6.0%
C 8779
 
5.5%
D 8490
 
5.4%
M 7549
 
4.8%
B 6785
 
4.3%
Other values (20) 50174
31.7%
Control
ValueCountFrequency (%)
Ÿ 6073
27.9%
” 3232
14.8%
€ 3079
14.1%
 1225
 
5.6%
“ 940
 
4.3%
’ 791
 
3.6%
‘ 718
 
3.3%
 717
 
3.3%
ƒ 690
 
3.2%
‹ 676
 
3.1%
Other values (18) 3644
16.7%
Other Punctuation
ValueCountFrequency (%)
/ 7715
41.1%
, 2740
 
14.6%
& 2615
 
13.9%
' 2113
 
11.3%
. 1463
 
7.8%
: 725
 
3.9%
¡ 432
 
2.3%
! 335
 
1.8%
\ 176
 
0.9%
· 100
 
0.5%
Other values (6) 346
 
1.8%
Decimal Number
ValueCountFrequency (%)
0 17952
40.2%
2 11659
26.1%
1 5401
 
12.1%
9 5018
 
11.2%
8 2035
 
4.6%
7 1232
 
2.8%
5 560
 
1.3%
4 350
 
0.8%
6 260
 
0.6%
3 169
 
0.4%
Math Symbol
ValueCountFrequency (%)
| 3824
92.5%
± 118
 
2.9%
> 97
 
2.3%
< 97
 
2.3%
Other Number
ValueCountFrequency (%)
³ 192
36.5%
¹ 122
23.2%
½ 112
21.3%
¼ 100
19.0%
Modifier Symbol
ValueCountFrequency (%)
¸ 105
29.7%
¯ 97
27.5%
¨ 85
24.1%
´ 66
18.7%
Currency Symbol
ValueCountFrequency (%)
¥ 2923
83.0%
¤ 307
 
8.7%
£ 292
 
8.3%
Other Symbol
ValueCountFrequency (%)
© 432
61.2%
¦ 176
24.9%
° 98
 
13.9%
Space Separator
ValueCountFrequency (%)
111011
99.9%
  85
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 1356
91.4%
] 127
 
8.6%
Open Punctuation
ValueCountFrequency (%)
( 1356
91.4%
[ 127
 
8.6%
Other Letter
ValueCountFrequency (%)
º 397
67.5%
ª 191
32.5%
Dash Punctuation
ValueCountFrequency (%)
- 8080
100.0%
Final Punctuation
ValueCountFrequency (%)
» 357
100.0%
Format
ValueCountFrequency (%)
­ 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 627165
74.3%
Common 217262
 
25.7%

Most frequent character per script

Common
ValueCountFrequency (%)
111011
51.1%
0 17952
 
8.3%
2 11659
 
5.4%
- 8080
 
3.7%
/ 7715
 
3.6%
Ÿ 6073
 
2.8%
1 5401
 
2.5%
9 5018
 
2.3%
| 3824
 
1.8%
” 3232
 
1.5%
Other values (72) 37297
 
17.2%
Latin
ValueCountFrequency (%)
o 53679
 
8.6%
e 46675
 
7.4%
a 39825
 
6.4%
s 39279
 
6.3%
i 37137
 
5.9%
n 29115
 
4.6%
t 28876
 
4.6%
r 28395
 
4.5%
p 28050
 
4.5%
c 21610
 
3.4%
Other values (52) 274524
43.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 803155
95.1%
None 41272
 
4.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111011
 
13.8%
o 53679
 
6.7%
e 46675
 
5.8%
a 39825
 
5.0%
s 39279
 
4.9%
i 37137
 
4.6%
n 29115
 
3.6%
t 28876
 
3.6%
r 28395
 
3.5%
p 28050
 
3.5%
Other values (73) 361113
45.0%
None
ValueCountFrequency (%)
ð 6073
14.7%
Ÿ 6073
14.7%
â 3803
 
9.2%
” 3232
 
7.8%
€ 3079
 
7.5%
¥ 2923
 
7.1%
 1225
 
3.0%
à 1122
 
2.7%
“ 940
 
2.3%
’ 791
 
1.9%
Other values (51) 12011
29.1%
Distinct471
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:12.945970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters722326
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row37i9dQZF1DXcZDD7cfEKhW
2nd row37i9dQZF1DXcZDD7cfEKhW
3rd row37i9dQZF1DXcZDD7cfEKhW
4th row37i9dQZF1DXcZDD7cfEKhW
5th row37i9dQZF1DXcZDD7cfEKhW
ValueCountFrequency (%)
4jkkvmpvl4lsioqqjeal0q 247
 
0.8%
37i9dqzf1dwthm4kx49uks 198
 
0.6%
6knqdwp0syvhfhor4lwp7x 195
 
0.6%
3xmqtdloigvj3lwh5e5x6f 189
 
0.6%
3ho3io0ijykgeqnbjb2sic 182
 
0.6%
25butzrvb1zj1mjioms09d 109
 
0.3%
3ykxidklz1eypvugofld1e 100
 
0.3%
3j2osvmeceao5nmo9jz5df 100
 
0.3%
4znaywuatxcaa9gaxvnfnq 100
 
0.3%
5ck0fshhcik1vwyecc0zat 100
 
0.3%
Other values (461) 31313
95.4%
2024-01-26T15:20:13.223291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 19178
 
2.7%
1 18762
 
2.6%
7 18699
 
2.6%
Q 18247
 
2.5%
D 17097
 
2.4%
i 17039
 
2.4%
d 16004
 
2.2%
9 15566
 
2.2%
Z 15453
 
2.1%
F 15033
 
2.1%
Other values (52) 551248
76.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 295950
41.0%
Lowercase Letter 273118
37.8%
Decimal Number 153258
21.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Q 18247
 
6.2%
D 17097
 
5.8%
Z 15453
 
5.2%
F 15033
 
5.1%
X 14138
 
4.8%
M 12611
 
4.3%
W 12506
 
4.2%
P 11361
 
3.8%
V 11049
 
3.7%
T 10854
 
3.7%
Other values (16) 157601
53.3%
Lowercase Letter
ValueCountFrequency (%)
i 17039
 
6.2%
d 16004
 
5.9%
k 11372
 
4.2%
j 11311
 
4.1%
v 11288
 
4.1%
c 10993
 
4.0%
o 10921
 
4.0%
y 10909
 
4.0%
l 10718
 
3.9%
r 10581
 
3.9%
Other values (16) 151982
55.6%
Decimal Number
ValueCountFrequency (%)
3 19178
12.5%
1 18762
12.2%
7 18699
12.2%
9 15566
10.2%
6 14798
9.7%
0 14703
9.6%
2 14260
9.3%
4 14200
9.3%
5 12144
7.9%
8 10948
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 569068
78.8%
Common 153258
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q 18247
 
3.2%
D 17097
 
3.0%
i 17039
 
3.0%
d 16004
 
2.8%
Z 15453
 
2.7%
F 15033
 
2.6%
X 14138
 
2.5%
M 12611
 
2.2%
W 12506
 
2.2%
k 11372
 
2.0%
Other values (42) 419568
73.7%
Common
ValueCountFrequency (%)
3 19178
12.5%
1 18762
12.2%
7 18699
12.2%
9 15566
10.2%
6 14798
9.7%
0 14703
9.6%
2 14260
9.3%
4 14200
9.3%
5 12144
7.9%
8 10948
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 722326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 19178
 
2.7%
1 18762
 
2.6%
7 18699
 
2.6%
Q 18247
 
2.5%
D 17097
 
2.4%
i 17039
 
2.4%
d 16004
 
2.2%
9 15566
 
2.2%
Z 15453
 
2.1%
F 15033
 
2.1%
Other values (52) 551248
76.3%

playlist_genre
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
edm
6043 
rap
5746 
pop
5507 
r&b
5431 
latin
5155 

Length

Max length5
Median length3
Mean length3.4648067
Min length3

Characters and Unicode

Total characters113760
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpop
2nd rowpop
3rd rowpop
4th rowpop
5th rowpop

Common Values

ValueCountFrequency (%)
edm 6043
18.4%
rap 5746
17.5%
pop 5507
16.8%
r&b 5431
16.5%
latin 5155
15.7%
rock 4951
15.1%

Length

2024-01-26T15:20:13.353393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-26T15:20:13.467062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
edm 6043
18.4%
rap 5746
17.5%
pop 5507
16.8%
r&b 5431
16.5%
latin 5155
15.7%
rock 4951
15.1%

Most occurring characters

ValueCountFrequency (%)
p 16760
14.7%
r 16128
14.2%
a 10901
9.6%
o 10458
9.2%
e 6043
 
5.3%
d 6043
 
5.3%
m 6043
 
5.3%
& 5431
 
4.8%
b 5431
 
4.8%
l 5155
 
4.5%
Other values (5) 25367
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 108329
95.2%
Other Punctuation 5431
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 16760
15.5%
r 16128
14.9%
a 10901
10.1%
o 10458
9.7%
e 6043
 
5.6%
d 6043
 
5.6%
m 6043
 
5.6%
b 5431
 
5.0%
l 5155
 
4.8%
t 5155
 
4.8%
Other values (4) 20212
18.7%
Other Punctuation
ValueCountFrequency (%)
& 5431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108329
95.2%
Common 5431
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 16760
15.5%
r 16128
14.9%
a 10901
10.1%
o 10458
9.7%
e 6043
 
5.6%
d 6043
 
5.6%
m 6043
 
5.6%
b 5431
 
5.0%
l 5155
 
4.8%
t 5155
 
4.8%
Other values (4) 20212
18.7%
Common
ValueCountFrequency (%)
& 5431
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 16760
14.7%
r 16128
14.2%
a 10901
9.6%
o 10458
9.2%
e 6043
 
5.3%
d 6043
 
5.3%
m 6043
 
5.3%
& 5431
 
4.8%
b 5431
 
4.8%
l 5155
 
4.5%
Other values (5) 25367
22.3%
Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
progressive electro house
 
1809
southern hip hop
 
1675
indie poptimism
 
1672
latin hip hop
 
1656
neo soul
 
1637
Other values (19)
24384 

Length

Max length25
Median length15
Mean length11.548625
Min length4

Characters and Unicode

Total characters379176
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdance pop
2nd rowdance pop
3rd rowdance pop
4th rowdance pop
5th rowdance pop

Common Values

ValueCountFrequency (%)
progressive electro house 1809
 
5.5%
southern hip hop 1675
 
5.1%
indie poptimism 1672
 
5.1%
latin hip hop 1656
 
5.0%
neo soul 1637
 
5.0%
pop edm 1517
 
4.6%
electro house 1511
 
4.6%
hard rock 1485
 
4.5%
gangster rap 1458
 
4.4%
electropop 1408
 
4.3%
Other values (14) 17005
51.8%

Length

2024-01-26T15:20:13.575520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pop 6462
 
9.6%
hip 5909
 
8.8%
hop 4653
 
6.9%
rock 3846
 
5.7%
electro 3320
 
5.0%
house 3320
 
5.0%
latin 2918
 
4.4%
progressive 1809
 
2.7%
southern 1675
 
2.5%
poptimism 1672
 
2.5%
Other values (24) 31419
46.9%

Most occurring characters

ValueCountFrequency (%)
o 41435
10.9%
p 39131
 
10.3%
e 35660
 
9.4%
34170
 
9.0%
r 28322
 
7.5%
i 22247
 
5.9%
t 20747
 
5.5%
a 20659
 
5.4%
n 20022
 
5.3%
s 18234
 
4.8%
Other values (14) 98549
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 343877
90.7%
Space Separator 34170
 
9.0%
Dash Punctuation 1129
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 41435
12.0%
p 39131
11.4%
e 35660
10.4%
r 28322
 
8.2%
i 22247
 
6.5%
t 20747
 
6.0%
a 20659
 
6.0%
n 20022
 
5.8%
s 18234
 
5.3%
h 17042
 
5.0%
Other values (12) 80378
23.4%
Space Separator
ValueCountFrequency (%)
34170
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 343877
90.7%
Common 35299
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 41435
12.0%
p 39131
11.4%
e 35660
10.4%
r 28322
 
8.2%
i 22247
 
6.5%
t 20747
 
6.0%
a 20659
 
6.0%
n 20022
 
5.8%
s 18234
 
5.3%
h 17042
 
5.0%
Other values (12) 80378
23.4%
Common
ValueCountFrequency (%)
34170
96.8%
- 1129
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 379176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 41435
10.9%
p 39131
 
10.3%
e 35660
 
9.4%
34170
 
9.0%
r 28322
 
7.5%
i 22247
 
5.9%
t 20747
 
5.5%
a 20659
 
5.4%
n 20022
 
5.3%
s 18234
 
4.8%
Other values (14) 98549
26.0%

danceability
Real number (ℝ)

Distinct822
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65484952
Minimum0
Maximum0.983
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:13.682951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.392
Q10.563
median0.672
Q30.761
95-th percentile0.868
Maximum0.983
Range0.983
Interquartile range (IQR)0.198

Descriptive statistics

Standard deviation0.14508532
Coefficient of variation (CV)0.22155521
Kurtosis0.010202119
Mean0.65484952
Median Absolute Deviation (MAD)0.098
Skewness-0.50448844
Sum21500.674
Variance0.02104975
MonotonicityNot monotonic
2024-01-26T15:20:13.807499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.733 118
 
0.4%
0.708 115
 
0.4%
0.704 112
 
0.3%
0.694 112
 
0.3%
0.784 111
 
0.3%
0.69 111
 
0.3%
0.701 111
 
0.3%
0.655 110
 
0.3%
0.676 110
 
0.3%
0.689 109
 
0.3%
Other values (812) 31714
96.6%
ValueCountFrequency (%)
0 1
< 0.1%
0.0771 1
< 0.1%
0.0787 1
< 0.1%
0.0985 1
< 0.1%
0.116 1
< 0.1%
0.118 1
< 0.1%
0.13 1
< 0.1%
0.135 2
< 0.1%
0.14 2
< 0.1%
0.141 1
< 0.1%
ValueCountFrequency (%)
0.983 1
 
< 0.1%
0.981 1
 
< 0.1%
0.979 2
 
< 0.1%
0.978 1
 
< 0.1%
0.977 1
 
< 0.1%
0.975 2
 
< 0.1%
0.974 5
< 0.1%
0.973 4
< 0.1%
0.972 2
 
< 0.1%
0.971 2
 
< 0.1%

energy
Real number (ℝ)

Distinct952
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69861927
Minimum0.000175
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:13.931607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.000175
5-th percentile0.366
Q10.581
median0.721
Q30.84
95-th percentile0.949
Maximum1
Range0.999825
Interquartile range (IQR)0.259

Descriptive statistics

Standard deviation0.18091003
Coefficient of variation (CV)0.25895368
Kurtosis0.000528152
Mean0.69861927
Median Absolute Deviation (MAD)0.128
Skewness-0.63632984
Sum22937.767
Variance0.03272844
MonotonicityNot monotonic
2024-01-26T15:20:14.057088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.787 100
 
0.3%
0.828 99
 
0.3%
0.833 98
 
0.3%
0.795 91
 
0.3%
0.711 91
 
0.3%
0.726 91
 
0.3%
0.869 89
 
0.3%
0.758 89
 
0.3%
0.76 88
 
0.3%
0.887 87
 
0.3%
Other values (942) 31910
97.2%
ValueCountFrequency (%)
0.000175 1
< 0.1%
0.00814 1
< 0.1%
0.0118 1
< 0.1%
0.0161 1
< 0.1%
0.0167 1
< 0.1%
0.0286 1
< 0.1%
0.0297 1
< 0.1%
0.0323 1
< 0.1%
0.036 1
< 0.1%
0.0375 1
< 0.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
0.999 7
 
< 0.1%
0.998 5
 
< 0.1%
0.997 6
 
< 0.1%
0.996 10
 
< 0.1%
0.995 13
< 0.1%
0.994 11
 
< 0.1%
0.993 30
0.1%
0.992 17
0.1%
0.991 20
0.1%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3744708
Minimum0
Maximum11
Zeros3454
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:14.158061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6116574
Coefficient of variation (CV)0.67200242
Kurtosis-1.307069
Mean5.3744708
Median Absolute Deviation (MAD)3
Skewness-0.023909144
Sum176460
Variance13.044069
MonotonicityNot monotonic
2024-01-26T15:20:14.247684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 4010
12.2%
0 3454
10.5%
7 3352
10.2%
9 3027
9.2%
11 2996
9.1%
2 2827
8.6%
5 2680
8.2%
6 2670
8.1%
8 2430
7.4%
10 2273
6.9%
Other values (2) 3114
9.5%
ValueCountFrequency (%)
0 3454
10.5%
1 4010
12.2%
2 2827
8.6%
3 913
 
2.8%
4 2201
6.7%
5 2680
8.2%
6 2670
8.1%
7 3352
10.2%
8 2430
7.4%
9 3027
9.2%
ValueCountFrequency (%)
11 2996
9.1%
10 2273
6.9%
9 3027
9.2%
8 2430
7.4%
7 3352
10.2%
6 2670
8.1%
5 2680
8.2%
4 2201
6.7%
3 913
 
2.8%
2 2827
8.6%

loudness
Real number (ℝ)

Distinct10222
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.7194991
Minimum-46.448
Maximum1.275
Zeros0
Zeros (%)0.0%
Negative32827
Negative (%)> 99.9%
Memory size256.6 KiB
2024-01-26T15:20:14.352622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-46.448
5-th percentile-12.4502
Q1-8.171
median-6.166
Q3-4.645
95-th percentile-2.972
Maximum1.275
Range47.723
Interquartile range (IQR)3.526

Descriptive statistics

Standard deviation2.9884364
Coefficient of variation (CV)-0.44474094
Kurtosis4.4909579
Mean-6.7194991
Median Absolute Deviation (MAD)1.703
Skewness-1.364097
Sum-220621.32
Variance8.930752
MonotonicityNot monotonic
2024-01-26T15:20:14.470430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.973 20
 
0.1%
-4.443 20
 
0.1%
-5.608 20
 
0.1%
-6.4 20
 
0.1%
-3.782 20
 
0.1%
-6.406 18
 
0.1%
-4.576 18
 
0.1%
-5.041 18
 
0.1%
-5.576 18
 
0.1%
-6.146 16
 
< 0.1%
Other values (10212) 32645
99.4%
ValueCountFrequency (%)
-46.448 1
< 0.1%
-36.624 1
< 0.1%
-36.509 1
< 0.1%
-35.96 1
< 0.1%
-35.427 1
< 0.1%
-34.283 1
< 0.1%
-29.561 1
< 0.1%
-28.309 1
< 0.1%
-26.279 1
< 0.1%
-26.207 1
< 0.1%
ValueCountFrequency (%)
1.275 1
< 0.1%
1.135 1
< 0.1%
0.642 1
< 0.1%
0.551 1
< 0.1%
0.326 1
< 0.1%
0.302 1
< 0.1%
-0.046 1
< 0.1%
-0.073 1
< 0.1%
-0.155 1
< 0.1%
-0.158 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size256.6 KiB
1
18574 
0
14259 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32833
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Length

2024-01-26T15:20:14.572657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-26T15:20:14.652577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Most occurring characters

ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32833
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Most occurring scripts

ValueCountFrequency (%)
Common 32833
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18574
56.6%
0 14259
43.4%

speechiness
Real number (ℝ)

Distinct1270
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10706807
Minimum0
Maximum0.918
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:14.749484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0298
Q10.041
median0.0625
Q30.132
95-th percentile0.3324
Maximum0.918
Range0.918
Interquartile range (IQR)0.091

Descriptive statistics

Standard deviation0.10131413
Coefficient of variation (CV)0.94625907
Kurtosis4.2608346
Mean0.10706807
Median Absolute Deviation (MAD)0.0274
Skewness1.9670285
Sum3515.3659
Variance0.010264553
MonotonicityNot monotonic
2024-01-26T15:20:14.864495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.102 116
 
0.4%
0.103 98
 
0.3%
0.109 93
 
0.3%
0.0354 93
 
0.3%
0.112 89
 
0.3%
0.107 88
 
0.3%
0.0346 88
 
0.3%
0.123 87
 
0.3%
0.0363 85
 
0.3%
0.106 85
 
0.3%
Other values (1260) 31911
97.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.0224 2
 
< 0.1%
0.0225 1
 
< 0.1%
0.0228 5
< 0.1%
0.023 1
 
< 0.1%
0.0231 1
 
< 0.1%
0.0232 4
< 0.1%
0.0233 2
 
< 0.1%
0.0234 3
< 0.1%
0.0235 6
< 0.1%
ValueCountFrequency (%)
0.918 1
< 0.1%
0.877 1
< 0.1%
0.869 2
< 0.1%
0.865 1
< 0.1%
0.86 1
< 0.1%
0.856 1
< 0.1%
0.855 1
< 0.1%
0.853 1
< 0.1%
0.817 1
< 0.1%
0.792 1
< 0.1%

acousticness
Real number (ℝ)

Distinct3731
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17533372
Minimum0
Maximum0.994
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:14.981651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0006306
Q10.0151
median0.0804
Q30.255
95-th percentile0.682
Maximum0.994
Range0.994
Interquartile range (IQR)0.2399

Descriptive statistics

Standard deviation0.21963254
Coefficient of variation (CV)1.2526543
Kurtosis1.8784089
Mean0.17533372
Median Absolute Deviation (MAD)0.07623
Skewness1.5947859
Sum5756.7319
Variance0.048238453
MonotonicityNot monotonic
2024-01-26T15:20:15.102668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.102 80
 
0.2%
0.128 80
 
0.2%
0.101 78
 
0.2%
0.114 74
 
0.2%
0.141 71
 
0.2%
0.107 70
 
0.2%
0.125 69
 
0.2%
0.104 69
 
0.2%
0.122 68
 
0.2%
0.11 65
 
0.2%
Other values (3721) 32109
97.8%
ValueCountFrequency (%)
0 1
< 0.1%
1.4 × 10-61
< 0.1%
1.44 × 10-61
< 0.1%
1.47 × 10-61
< 0.1%
1.66 × 10-61
< 0.1%
2.16 × 10-61
< 0.1%
2.22 × 10-61
< 0.1%
2.32 × 10-61
< 0.1%
2.43 × 10-61
< 0.1%
2.46 × 10-61
< 0.1%
ValueCountFrequency (%)
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.989 3
< 0.1%
0.986 2
< 0.1%
0.985 2
< 0.1%
0.984 2
< 0.1%
0.983 3
< 0.1%
0.982 1
 
< 0.1%
0.979 4
< 0.1%
0.978 3
< 0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct4729
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084747161
Minimum0
Maximum0.994
Zeros12089
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:15.221207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.61 × 10-5
Q30.00483
95-th percentile0.767
Maximum0.994
Range0.994
Interquartile range (IQR)0.00483

Descriptive statistics

Standard deviation0.22423012
Coefficient of variation (CV)2.6458718
Kurtosis6.2740615
Mean0.084747161
Median Absolute Deviation (MAD)1.61 × 10-5
Skewness2.7594718
Sum2782.5035
Variance0.050279149
MonotonicityNot monotonic
2024-01-26T15:20:15.436837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12089
36.8%
0.00106 30
 
0.1%
0.124 30
 
0.1%
1.21 × 10-626
 
0.1%
1.16 × 10-626
 
0.1%
1.17 × 10-523
 
0.1%
0.00016 23
 
0.1%
0.000115 22
 
0.1%
0.0114 22
 
0.1%
1.85 × 10-522
 
0.1%
Other values (4719) 20520
62.5%
ValueCountFrequency (%)
0 12089
36.8%
1 × 10-65
 
< 0.1%
1.01 × 10-617
 
0.1%
1.02 × 10-67
 
< 0.1%
1.03 × 10-614
 
< 0.1%
1.04 × 10-620
 
0.1%
1.05 × 10-69
 
< 0.1%
1.06 × 10-610
 
< 0.1%
1.07 × 10-613
 
< 0.1%
1.08 × 10-613
 
< 0.1%
ValueCountFrequency (%)
0.994 2
< 0.1%
0.987 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 1
 
< 0.1%
0.979 1
 
< 0.1%
0.974 2
< 0.1%
0.972 3
< 0.1%
0.971 2
< 0.1%
0.97 1
 
< 0.1%

liveness
Real number (ℝ)

Distinct1624
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1901762
Minimum0
Maximum0.996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:15.556936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0559
Q10.0927
median0.127
Q30.248
95-th percentile0.5104
Maximum0.996
Range0.996
Interquartile range (IQR)0.1553

Descriptive statistics

Standard deviation0.15431728
Coefficient of variation (CV)0.81144372
Kurtosis5.065937
Mean0.1901762
Median Absolute Deviation (MAD)0.0496
Skewness2.0767204
Sum6244.055
Variance0.023813823
MonotonicityNot monotonic
2024-01-26T15:20:15.679934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 346
 
1.1%
0.108 310
 
0.9%
0.11 305
 
0.9%
0.105 295
 
0.9%
0.104 294
 
0.9%
0.109 287
 
0.9%
0.106 284
 
0.9%
0.101 275
 
0.8%
0.112 272
 
0.8%
0.107 266
 
0.8%
Other values (1614) 29899
91.1%
ValueCountFrequency (%)
0 1
< 0.1%
0.00936 1
< 0.1%
0.00946 1
< 0.1%
0.0131 1
< 0.1%
0.015 2
< 0.1%
0.0155 2
< 0.1%
0.0158 1
< 0.1%
0.0163 1
< 0.1%
0.0165 1
< 0.1%
0.0167 1
< 0.1%
ValueCountFrequency (%)
0.996 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.991 2
 
< 0.1%
0.99 3
< 0.1%
0.988 5
< 0.1%
0.985 3
< 0.1%
0.984 1
 
< 0.1%
0.983 2
 
< 0.1%
0.982 1
 
< 0.1%

valence
Real number (ℝ)

Distinct1362
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51056097
Minimum0
Maximum0.991
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:15.800232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.132
Q10.331
median0.512
Q30.693
95-th percentile0.893
Maximum0.991
Range0.991
Interquartile range (IQR)0.362

Descriptive statistics

Standard deviation0.23314597
Coefficient of variation (CV)0.45664668
Kurtosis-0.90098076
Mean0.51056097
Median Absolute Deviation (MAD)0.181
Skewness-0.0054853502
Sum16763.248
Variance0.054357045
MonotonicityNot monotonic
2024-01-26T15:20:15.924717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 69
 
0.2%
0.58 68
 
0.2%
0.43 68
 
0.2%
0.562 68
 
0.2%
0.389 68
 
0.2%
0.499 68
 
0.2%
0.516 67
 
0.2%
0.392 66
 
0.2%
0.536 66
 
0.2%
0.347 66
 
0.2%
Other values (1352) 32159
97.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 × 10-55
< 0.1%
0.0116 1
 
< 0.1%
0.0122 1
 
< 0.1%
0.0139 1
 
< 0.1%
0.0159 1
 
< 0.1%
0.0223 1
 
< 0.1%
0.0234 1
 
< 0.1%
0.0269 1
 
< 0.1%
0.0276 1
 
< 0.1%
ValueCountFrequency (%)
0.991 1
 
< 0.1%
0.99 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.981 2
 
< 0.1%
0.98 1
 
< 0.1%
0.979 3
< 0.1%
0.978 1
 
< 0.1%
0.977 5
< 0.1%

tempo
Real number (ℝ)

Distinct17684
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.88113
Minimum0
Maximum239.44
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:16.043721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile81.0682
Q199.96
median121.984
Q3133.918
95-th percentile173.95
Maximum239.44
Range239.44
Interquartile range (IQR)33.958

Descriptive statistics

Standard deviation26.903624
Coefficient of variation (CV)0.22256264
Kurtosis0.08326436
Mean120.88113
Median Absolute Deviation (MAD)18.045
Skewness0.52887789
Sum3968890.2
Variance723.80499
MonotonicityNot monotonic
2024-01-26T15:20:16.164334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.992 45
 
0.1%
127.994 35
 
0.1%
127.993 33
 
0.1%
128.007 32
 
0.1%
128.003 31
 
0.1%
127.997 31
 
0.1%
128.001 31
 
0.1%
128.005 30
 
0.1%
127.991 29
 
0.1%
128.028 29
 
0.1%
Other values (17674) 32507
99.0%
ValueCountFrequency (%)
0 1
< 0.1%
35.477 1
< 0.1%
37.114 1
< 0.1%
38.985 1
< 0.1%
46.169 1
< 0.1%
48.718 2
< 0.1%
48.981 1
< 0.1%
49.597 1
< 0.1%
50.454 1
< 0.1%
52.017 1
< 0.1%
ValueCountFrequency (%)
239.44 1
< 0.1%
220.252 1
< 0.1%
219.991 1
< 0.1%
219.961 1
< 0.1%
214.516 1
< 0.1%
214.047 1
< 0.1%
214.017 1
< 0.1%
213.99 1
< 0.1%
212.137 2
< 0.1%
212.058 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct19785
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225799.81
Minimum4000
Maximum517810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2024-01-26T15:20:16.282608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile148394.8
Q1187819
median216000
Q3253585
95-th percentile337400
Maximum517810
Range513810
Interquartile range (IQR)65766

Descriptive statistics

Standard deviation59834.006
Coefficient of variation (CV)0.26498696
Kurtosis2.6991863
Mean225799.81
Median Absolute Deviation (MAD)31867
Skewness1.1498633
Sum7.4136852 × 109
Variance3.5801083 × 109
MonotonicityNot monotonic
2024-01-26T15:20:16.406838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240000 37
 
0.1%
192000 37
 
0.1%
210000 30
 
0.1%
180000 26
 
0.1%
195000 25
 
0.1%
160000 24
 
0.1%
225000 23
 
0.1%
203000 19
 
0.1%
168000 18
 
0.1%
172500 18
 
0.1%
Other values (19775) 32576
99.2%
ValueCountFrequency (%)
4000 1
< 0.1%
29493 1
< 0.1%
31429 1
< 0.1%
31875 1
< 0.1%
31893 1
< 0.1%
33750 2
< 0.1%
33900 1
< 0.1%
34560 1
< 0.1%
37500 1
< 0.1%
37640 1
< 0.1%
ValueCountFrequency (%)
517810 1
< 0.1%
517125 2
< 0.1%
516893 1
< 0.1%
516760 1
< 0.1%
515960 1
< 0.1%
515867 2
< 0.1%
515703 1
< 0.1%
515680 1
< 0.1%
513440 1
< 0.1%
513000 1
< 0.1%

Interactions

2024-01-26T15:20:07.256926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:56.841785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.785688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.709918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.635937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.556900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.469893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.470800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.406652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.354958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.294593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.232475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.336758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:56.922111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.864622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.787125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.711891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.632073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.546548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.550157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.486927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.434089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.373616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.310145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.416325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.001247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.941426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.868026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.788997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.710647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.623098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.628170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.564949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.511900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.453917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.389615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.497531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.081000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.019127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.944143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.865463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.787189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.698172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.706666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.642325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.590393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.532019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.467130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.575523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.158995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.095625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.020477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.941714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.863964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.774439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.784256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.719598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.668205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.609388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.545896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.652395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.240968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.170438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.095966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.015808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.936540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.847003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.859529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.796750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.744535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.686558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.620745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.728775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.316473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.244555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.170271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.090485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.009357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.919943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.935788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.877387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.819066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.761617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.695941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.807611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.394426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.322026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.248065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.168221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.087992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.092713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.012957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.958251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.898312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.841175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.865976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.890105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.473869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.400573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.325296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.246427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.165168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.166022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.092316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.038049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.977017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.919682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.943992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.969558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.551755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.476969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.402165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.325429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.242143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.241557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.171483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.115967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.053899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.998534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.023246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:08.049870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.630082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.554277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.479909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.402154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.318137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.316767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.250114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.195158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.134214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.075358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.103432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:08.131157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:57.707680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:58.631070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:19:59.556823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:00.479547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:01.394350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:02.393461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:03.327602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:04.274316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:05.212968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:06.152612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-26T15:20:07.178882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-26T15:20:08.272884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-26T15:20:08.562989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

track_idtrack_nametrack_artisttrack_popularitytrack_album_idtrack_album_nametrack_album_release_dateplaylist_nameplaylist_idplaylist_genreplaylist_subgenredanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_ms
06f807x0ima9a1j3VPbc7VNI Don't Care (with Justin Bieber) - Loud Luxury RemixEd Sheeran662oCs0DGTsRO98Gh5ZSl2CxI Don't Care (with Justin Bieber) [Loud Luxury Remix]2019-06-14Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.7480.9166-2.63410.05830.10200.0000000.06530.518122.036194754
10r7CVbZTWZgbTCYdfa2P31Memories - Dillon Francis RemixMaroon 56763rPSO264uRjW1X5E6cWv6Memories (Dillon Francis Remix)2019-12-13Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.7260.81511-4.96910.03730.07240.0042100.35700.69399.972162600
21z1Hg7Vb0AhHDiEmnDE79lAll the Time - Don Diablo RemixZara Larsson701HoSmj2eLcsrR0vE9gThr4All the Time (Don Diablo Remix)2019-07-05Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6750.9311-3.43200.07420.07940.0000230.11000.613124.008176616
375FpbthrwQmzHlBJLuGdC7Call You Mine - Keanu Silva RemixThe Chainsmokers601nqYsOef1yKKuGOVchbsk6Call You Mine - The Remixes2019-07-19Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.7180.9307-3.77810.10200.02870.0000090.20400.277121.956169093
41e8PAfcKUYoKkxPhrHqw4xSomeone You Loved - Future Humans RemixLewis Capaldi697m7vv9wlQ4i0LFuJiE2zsQSomeone You Loved (Future Humans Remix)2019-03-05Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6500.8331-4.67210.03590.08030.0000000.08330.725123.976189052
57fvUMiyapMsRRxr07cU8EfBeautiful People (feat. Khalid) - Jack Wins RemixEd Sheeran672yiy9cd2QktrNvWC2EUi0kBeautiful People (feat. Khalid) [Jack Wins Remix]2019-07-11Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6750.9198-5.38510.12700.07990.0000000.14300.585124.982163049
62OAylPUDDfwRGfe0lYqlCQNever Really Over - R3HAB RemixKaty Perry627INHYSeusaFlyrHSNxm8qHNever Really Over (R3HAB Remix)2019-07-26Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.4490.8565-4.78800.06230.18700.0000000.17600.152112.648187675
76b1RNvAcJjQH73eZO4BLABPost Malone (feat. RANI) - GATTÜSO RemixSam Feldt696703SRPsLkS4bPtMFFJes1Post Malone (feat. RANI) [GATTÜSO Remix]2019-08-29Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.5420.9034-2.41900.04340.03350.0000050.11100.367127.936207619
87bF6tCO3gFb8INrEDcjNT5Tough Love - Tiësto Remix / Radio EditAvicii687CvAfGvq4RlIwEbT9o8IavTough Love (Tiësto Remix)2019-06-14Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.5940.9358-3.56210.05650.02490.0000040.63700.366127.015193187
91IXGILkPm0tOCNeq00kCPaIf I Can't Have You - Gryffin RemixShawn Mendes674QxzbfSsVryEQwvPFEV5IuIf I Can't Have You (Gryffin Remix)2019-06-20Pop Remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6420.8182-4.55210.03200.05670.0000000.09190.590124.957253040
track_idtrack_nametrack_artisttrack_popularitytrack_album_idtrack_album_nametrack_album_release_dateplaylist_nameplaylist_idplaylist_genreplaylist_subgenredanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_ms
328230coMLoVcagZPGF5zxc5RF8Everybody Is In The Place - Radio EditHardwell281PdMbB6qgSzS9zcT9xP6KxEverybody Is In The Place (Radio Edit)2014-04-18♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.6090.9892-3.51010.08670.0004340.2190000.07150.0358130.046171697
328243zKST4nk4QJE77oLjUZ0NgHey BrotherAvicii2002h9kO2oLKnLtycgbElKswTrue2013-01-01♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5450.7807-4.86700.04360.0309000.0000460.08280.4580125.014255093
328252EpS5TgdngSISM63rhBsnKBooyah - Radio EditShowtek470Dix8CfvtZEHUyJGnmPnaBBooyah2013-01-01♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5590.91611-3.05010.06260.0453000.0000130.22500.1950128.012215295
328261EavLSmwRWtmkKEmlCfFzTWastedTiësto47584m4QL0kmpG69zSpMKvv8Wasted2014-04-22♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.6450.8322-5.59510.02940.0010600.0026400.19900.3750112.028188371
328270aBDrRTgDCwWbcOnEIp7DJMany Ways - Radio EditFerry Corsten feat. Jenny Wahlstrom2759XOfNjuYZB6feC6QUzS3eMany Ways2013♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5810.6405-8.36710.03650.0266000.0000000.57200.2880128.001196993
328287bxnKAamR3snQ1VGLuVfC1City Of Lights - Official Radio EditLush & Simon422azRoBBWEEEYhqV6sb7JrTCity Of Lights (Vocal Mix)2014-04-28♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.4280.9222-1.81410.09360.0766000.0000000.06680.2100128.170204375
328295Aevni09Em4575077nkWHzCloser - Sultan & Ned Shepard RemixTegan and Sara206kD6KLxj7s8eCE3ABvAyf5Closer Remixed2013-03-08♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5220.7860-4.46210.04200.0017100.0042700.37500.4000128.041353120
328307ImMqPP3Q1yfUHvsdn7wEoSweet Surrender - Radio EditStarkillers140ltWNSY9JgxoIZO4VzuCa6Sweet Surrender (Radio Edit)2014-04-21♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5290.8216-4.89900.04810.1080000.0000010.15000.4360127.989210112
328312m69mhnfQ1Oq6lGtXuYhgXOnly For You - Maor Levi RemixMat Zo151fGrOkHnHJcStl14zNx8JyOnly For You (Remixes)2014-01-01♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.6260.8882-3.36110.10900.0079200.1270000.34300.3080128.008367432
3283229zWqhca3zt5NsckZqDf6cTyphoon - Original MixJulian Calor270X3mUOm6MhxR7PzxG95rAoTyphoon/Storm2014-03-03♥ EDM LOVE 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.6030.8845-4.57100.03850.0001330.3410000.74200.0894127.984337500